Evaluation of the Interaction-Based Hazard Index Formula Using Data on Four Trihalomethanes from U.S. EPA's Multiple-Purpose Design Study.

The interaction-based hazard index (HIINT), a mixtures approach to characterizing toxicologic interactions, is demonstrated and evaluated by statistically analyzing data on four regulated trihalomethanes (THMs). These THMs were the subject of a multipurpose toxicology study specifically designed to evaluate the HIINT formula. This HIINT evaluation uses single, binary and quaternary mixture THM data. While this research is considered preliminary, the results provide insights on the application of HIINT when toxicology mixture data are available and on improvements to the method. The results for relative liver weight show the HIINT was generally not conservative but did adjust the additive hazard index (HI) in the correct direction, predicting greater than dose-additivity, as seen in the mixture data. For the liver serum enzyme endpoint alanine aminotransferase, the results were mixed, with some indices giving an estimated effective dose lower than the observed mixture effective dose and others higher; in general, the HIINT adjusted the HI in the correct direction, predicting less than dose-additivity. In addition, a methodological improvement was made in the calculation of maximum interaction magnitude. Suggested refinements to the HIINT included mixture-specific replacements for default parameter values and approaches for supplementing the usual qualitative discussions of uncertainty with numerical descriptions.

Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference.

Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior. Thus, generalizable, interpretable, computational models of adaptive human driving behavior are still rare. This paper proposes such a model based on active inference, a behavioral modeling framework originating in computational neuroscience. The model offers a principled solution to how humans trade progress against caution through policy selection based on the single mandate to minimize expected free energy. This casts goal-seeking and information-seeking (uncertainty-resolving) behavior under a single objective function, allowing the model to seamlessly resolve uncertainty as a means to obtain its goals. We apply the model in two apparently disparate driving scenarios that require managing uncertainty, (1) driving past an occluding object and (2) visual time-sharing between driving and a secondary task, and show how human-like adaptive driving behavior emerges from the single principle of expected free energy minimization.

Evaluation of a Proportional Response Addition Approach to Mixture Risk Assessment and Predictive Toxicology Using Data on Four Trihalomethanes from the U.S. EPA's Multiple-Purpose Design Study.

In this study, proportional response addition (Prop-RA), a model for predicting response from chemical mixture exposure, is demonstrated and evaluated by statistically analyzing data on all possible binary combinations of the four regulated trihalomethanes (THMs). These THMs were the subject of a multipurpose toxicology study specifically designed to evaluate Prop-RA. The experimental design used a set of doses common to all components and mixtures, providing hepatotoxicity data on the four single THMs and the binary combinations. In Prop-RA, the contribution of each component to mixture toxicity is proportional to its fraction in the mixture based on its response at the total mixture dose. The primary analysis consisted of 160 evaluations. Statistically significant departures from the Prop-RA prediction were found for seven evaluations, with three predications that were greater than and four that were less than the predicted response; interaction magnitudes (n-fold difference in response vs. prediction) ranged from 1.3 to 1.4 for the former and 2.6 to 3.8 for the latter. These predictions support the idea that Prop-RA works best with chemicals where the effective dose ranges overlap. Prop-RA does not assume the similarity of toxic action or independence, but it can be applied to a mixture of components that affect the same organ/system, with perhaps unknown toxic modes of action.

Label-Free, Noninvasive Bone Cell Classification by Hyperspectral Confocal Raman Microscopy.

Characterizing and identifying cells in multicellular in vitro models remain a substantial challenge. Here, we utilize hyperspectral confocal Raman microscopy and principal component analysis coupled with linear discriminant analysis to form a label-free, noninvasive approach for classifying bone cells and osteosarcoma cells. Through the development of a library of hyperspectral Raman images of the K7M2-wt osteosarcoma cell lines, 7F2 osteoblast cell lines, RAW 264.7 macrophage cell line, and osteoclasts induced from RAW 264.7 macrophages, we built a linear discriminant model capable of correctly identifying each of these cell types. The model was cross-validated using a k-fold cross validation scheme. The results show a minimum of 72% accuracy in predicting cell type. We also utilize the model to reconstruct the spectra of K7M2 and 7F2 to determine whether osteosarcoma cancer cells and normal osteoblasts have any prominent differences that can be captured by Raman. We find that the main differences between these two cell types are the prominence of the ß-sheet protein secondary structure in K7M2 versus the α-helix protein secondary structure in 7F2. Additionally, differences in the CH2 deformation Raman feature highlight that the membrane lipid structure is different between these cells, which may affect the overall signaling and functional contrasts. Overall, we show that hyperspectral confocal Raman microscopy can serve as an effective tool for label-free, nondestructive cellular classification and that the spectral reconstructions can be used to gain deeper insight into the differences that drive different functional outcomes of different cells.

Thermal Transport and Mechanical Stress Mapping of a Compression Bonded GaN/Diamond Interface for Vertical Power Devices.

Bonding diamond to the back side of gallium nitride (GaN) electronics has been shown to improve thermal management in lateral devices; however, engineering challenges remain with the bonding process and characterizing the bond quality for vertical device architectures. Here, integration of these two materials is achieved by room-temperature compression bonding centimeter-scale GaN and a diamond die via an intermetallic bonding layer of Ti/Au. Recent attempts at GaN/diamond bonding have utilized a modified surface activation bonding (SAB) method, which requires Ar fast atom bombardment immediately followed by bonding within the same tool under ultrahigh vacuum (UHV) conditions. The method presented here does not require a dedicated SAB tool yet still achieves bonding via a room-temperature metal-metal compression process. Imaging of the buried interface and the total bonding area is achieved via transmission electron microscopy (TEM) and confocal acoustic scanning microscopy (C-SAM), respectively. The thermal transport quality of the bond is extracted from spatially resolved frequency-domain thermoreflectance (FDTR) with the bonded areas boasting a thermal boundary conductance of >100 MW/m2·K. Additionally, Raman maps of GaN near the GaN-diamond interface reveal a low level of compressive stress, <80 MPa, in well-bonded regions. FDTR and Raman were coutilized to map these buried interfaces and revealed some poor thermally bonded areas bordered by high-stress regions, highlighting the importance of spatial sampling for a complete picture of bond quality. Overall, this work demonstrates a novel method for thermal management in vertical GaN devices that maintains low intrinsic stresses while boasting high thermal boundary conductances.

Automated vehicles for older adults with cognitive impairment: a survey study.

As the population is ageing, the number of older adults with cognitive impairment (CI) is increasing. Automated vehicles (AVs) can improve independence and enhance the mobility of these individuals. This study aimed to: (1) understand the perception of older adults (with and without CI) and stakeholders providing services and supports regarding care and transportation about AVs, and (2) suggest potential solutions to improve the perception of AVs for older adults with mild or moderate CI. A survey was conducted with 435 older adults with and without CI and 188 stakeholders (e.g. caregivers). The results were analysed using partial least square - structural equation modelling and multiple correspondence analysis. The findings suggested relationships between older adults' level of cognitive impairment, mobility, knowledge of AVs, and perception of AVs. The results provided recommendations to improve older adults' perception of AVs including education and adaptive driving simulation-based training.Practitioner summary: This study investigated the perception of older adults and other stakeholders regarding AVs. The findings suggested relationships between older adults' level of cognitive impairment, mobility, knowledge of AVs, and perception of AVs. The results provided guidelines to improve older adults' perception of AVs.

Driving among individuals with chronic conditions: A systematic review of applied research using kinematic driving sensors.

BACKGROUND: Kinematic driving data studies are a novel methodology relevant to health care, but prior studies have considerable variance in their methods, populations, and findings suggesting a need for critical analysis and appraisal for feasibility and methodological guidelines. METHODS: We assessed kinematic driving studies of adults with chronic conditions for study feasibility, characteristics, and key findings, to generate recommendations for future study designs, and to identify promising directions for applications of kinematic driving data. PRISMA was used to guide the review and searches included PubMed, CINAHL, and Compendex. Of 379 abstract/titles screened, 49 full-text articles were reviewed, and 29 articles met inclusion criteria of analyzing trip-level kinematic driving data from adult drivers with chronic conditions. RESULTS: The predominant chronic conditions studied were Alzheimer's disease and related Dementias, obstructive sleep apnea, and diabetes mellitus. Study objectives included feasibility testing of kinematic driving data collection in the context of chronic conditions, comparisons of simulation with real-world kinematic driving behavior, assessments of driving behavior effects associated with chronic conditions, and prognostication or disease classification drawn from kinematic driving data. Across the studies, there was no consensus on devices, measures, or sampling parameters; however, studies showed evidence that driving behavior could reliably differentiate between adults with chronic conditions and healthy controls. CONCLUSIONS: Vehicle sensors can provide driver-specific measures relevant to clinical assessment and interventions. Using kinematic driving data to assess and address driving measures of individuals with multiple chronic conditions is positioned to amplify a functional outcome measure that matters to patients.

Inversion for Thermal Properties with Frequency Domain Thermoreflectance.

3D integration of multiple microelectronic devices improves size, weight, and power while increasing the number of interconnections between components. One integration method involves the use of metal bump bonds to connect devices and components on a common interposer platform. Significant variations in the coefficient of thermal expansion in such systems lead to stresses that can cause thermomechanical and electrical failures. More advanced characterization and failure analysis techniques are necessary to assess the bond quality between components. Frequency domain thermoreflectance (FDTR) is a nondestructive, noncontact testing method used to determine thermal properties in a sample by fitting the phase lag between an applied heat flux and the surface temperature response. The typical use of FDTR data involves fitting for thermal properties in geometries with a high degree of symmetry. In this work, finite element method simulations are performed using high performance computing codes to facilitate the modeling of samples with arbitrary geometric complexity. A gradient-based optimization technique is also presented to determine unknown thermal properties in a discretized domain. Using experimental FDTR data from a GaN-diamond sample, thermal conductivity is then determined in an unknown layer to provide a spatial map of bond quality at various points in the sample.

Driving decisions after critical illness: Qualitative analysis of patient-provider reviews during ICU recovery clinic assessments.

BACKGROUND: Driving a vehicle is a functional task requiring a threshold of physical, behavioral and cognitive skills. OBJECTIVE: To report patient-provider evaluations of driving status and driving safety assessments after critical illness. DESIGN: Qualitative secondary analysis of driving-related dialog drawn from a two-arm pilot study evaluating telemedicine delivery of Intensive Care Unit Recovery Clinic assessments. Multidisciplinary providers assessed physical, psychological, and cognitive recovery during one-hour telemedicine ICU-RC assessments. Qualitative secondary analysis of patient-provider dialog specific to driving practices after critical illness. SETTING AND PATIENTS: Multidisciplinary Intensive Care Unit Recovery clinic assessment dialog between 17 patients and their providers during 3-week and/or 12-week follow-up assessments at a tertiary academic medical center in the Southeastern United States. MAIN MEASURES AND KEY RESULTS: Thematic content analysis was performed to describe and classify driving safety discussion, driving status and driving practices after critical illness. Driving-related discussions occurred with 15 of 17 participants and were clinician-initiated. When assessed, driving status varied with participants reporting independent decisions to resume driving, delay driving and cease driving after critical illness. Patient-reported driving practices after critical illness included modifications to limit driving to medical appointments, self-assessments of trip durations, and inclusion of care partners as a safety measure for new onset fatigue while driving. CONCLUSION: We found that patients are largely self-navigating this stage of recovery, making subjective decisions on driving resumption and overall driving status. These results highlight that driving status changes are an often underrecognized yet salient social cost of critical illness. TRIAL REGISTRATION: Clinicaltrials.gov: NCT03926533.

Return to Driving After Critical Illness.

This cross-sectional study examines the postintensive care syndrome in patients who had vs patients who had not resumed driving 1 month after hospitalization for a critical illness.

Analysis of Silver Alert Reporting System Activations for Missing Adults With Dementia in Texas, 2017 to 2022.

This cross-sectional study analyzes data from Silver Alert activations in Texas from 2017 to 2022 to identify temporal, geographic, and wandering characteristics of missing adults with dementia.

A Bayesian Regression Analysis of the Effects of Alert Presence and Scenario Criticality on Automated Vehicle Takeover Performance.

OBJECTIVE: This study investigates the impact of silent and alerted failures on driver performance across two levels of scenario criticality during automated vehicle transitions of control. BACKGROUND: Recent analyses of automated vehicle crashes show that many crashes occur after a transition of control or a silent automation failure. A substantial amount of research has been dedicated to investigating the impact of various factors on drivers' responses, but silent failures and their interactions with scenario criticality are understudied. METHOD: A driving simulator study was conducted comparing scenario criticality, alert presence, and two driving scenarios. Bayesian regression models and Fisher's exact tests were used to investigate the impact of alert and scenario criticality on takeover performance. RESULTS: The results show that silent failures increase takeover times and the intensity of posttakeover maximum accelerations and decrease the posttakeover minimum time-to-collision. While the predicted average impact of silent failures on takeover time was practically low, the effects on minimum time-to-collision and maximum accelerations were safety-significant. The analysis of posttakeover control interaction effects shows that the effect of alert presence differs by the scenario criticality. CONCLUSION: Although the impact of the absence of an alert on takeover performance was less than that of scenario criticality, silent failures seem to play a substantial role-by leading to an unsafe maneuver-in critical automated vehicle takeovers. APPLICATION: Understanding the implications of silent failure on driver's takeover performance can benefit the assessment of automated vehicles' safety and provide guidance for fail-safe system designs.

Predicting Procedure Step Performance From Operator and Text Features: A Critical First Step Toward Machine Learning-Driven Procedure Design.

OBJECTIVE: The goal of this study is to assess machine learning for predicting procedure performance from operator and procedure characteristics. BACKGROUND: Procedures are vital for the performance and safety of high-risk industries. Current procedure design guidelines are insufficient because they rely on subjective assessments and qualitative analyses that struggle to integrate and quantify the diversity of factors that influence procedure performance. METHOD: We used data from a 25-participant study with four procedures, conducted on a high-fidelity oil extraction simulation to develop logistic regression (LR), random forest (RF), and decision tree (DT) algorithms that predict procedure step performance from operator, step, readability, and natural language processing-based features. Features were filtered using the Boruta approach. The algorithms were trained and optimized with a repeated 10-fold cross-validation. After training, inference was performed using variable importance and partial dependence plots. RESULTS: The RF, DT, and LR algorithms with all features had an area under the receiver operating characteristic curve (AUC) of 0.78, 0.77, and 0.75, respectively, and significantly outperformed the LR with only operator features (LROP), with an AUC of 0.61. The most important features were experience, familiarity, total words, and character-based metrics. The partial dependence plots showed that steps with fewer words, abbreviations, and characters were correlated with correct step performance. CONCLUSION: Machine learning algorithms are a promising approach for predicting step-level procedure performance, with acknowledged limitations on interpolating to nonobserved data, and may help guide procedure design after validation with additional data on further tasks. APPLICATION: After validation, the inferences from these models can be used to generate procedure design alternatives.

The Effects of Drowsiness Detection Technology and Education on Nurses' Beliefs and Attitudes toward Drowsy Driving.

Occupational ApplicationsNurses' perceived health threat from driving drowsy along with their attitude toward an intervention can be targeted to improve nurses' intentions to avoid this dangerous behavior. The evidence presented in this paper suggests that educational interventions that raise awareness of the risks of drowsy driving and its consequences (e.g., fatalities or injuries), as well as peer stories about their experiences, may positively affect nurses' perceived health threat and attitudes toward drowsy driving interventions.

Background Drowsy driving is prevalent among night-shift nurses, yet there is a gap in understanding nurses' beliefs and attitudes that may affect their intention to avoid drowsy driving.Objectives The objectives of the study were twofold: 1) investigate how behavioral constructs such as beliefs and attitudes may affect nurses' intention to avoid drowsy driving; and 2) assess changes in such beliefs and attitudes during a study that evaluated the effectiveness of educational and technological interventions.Methods Three-hundred night-shift nurses were recruited from a large hospital in Texas to participate in a randomized controlled trial. Participants were randomly assigned to three groups: 1) control; 2) educational intervention; and 3) combined educational and technological intervention. The study utilized an integrated model drawing from the constructs of the Theory of Planned Behavior and the Health Belief Model to elicit attitudes, beliefs, and intentions to use in-vehicle drowsiness detection technologies. Each group was surveyed pre- intervention and at post-intervention around 3 months later to assess changes in beliefs and attitudes. Structural equation models and path analysis were used to analyze changes in beliefs.Results Seventy-nine participants completed the pre-intervention questionnaire, and 44 nurses completed the pre- and post-intervention surveys. Intention was predicted primarily by attitude and perceived health threat. Perceived health threat also mediated the relationship between behavioral intention and the influence of subjective norms as well as perceived behavioral control. Participants who received education about drowsy driving had positive changes in beliefs.Conclusions Nurses' perceived health threat from driving drowsy and their attitude toward our intervention were important motivators to avoid drowsy driving. Interventions aiming at raising awareness of the risks associated with drowsy driving may be effective at motivating nurses to avoid drowsy driving.

Posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data.

Posttraumatic Stress Disorder (PTSD) is a psychiatric condition affecting nearly a quarter of the United States war veterans who return from war zones. Treatment for PTSD typically consists of a combination of in-session therapy and medication. However; patients often experience their most severe PTSD symptoms outside of therapy sessions. Mobile health applications may address this gap, but their effectiveness is limited by the current gap in continuous monitoring and detection capabilities enabling timely intervention. The goal of this article is to develop a novel method to detect hyperarousal events using physiological and activity-based machine learning algorithms. Physiological data including heart rate and body acceleration as well as self-reported hyperarousal events were collected using a tool developed for commercial off-the-shelf wearable devices from 99 United States veterans diagnosed with PTSD over several days. The data were used to develop four machine learning algorithms: Random Forest, Support Vector Machine, Logistic Regression and XGBoost. The XGBoost model had the best performance in detecting onset of PTSD symptoms with over 83% accuracy and an AUC of 0.70. Post-hoc SHapley Additive exPlanations (SHAP) additive explanation analysis showed that algorithm predictions were correlated with average heart rate, minimum heart rate and average body acceleration. Findings show promise in detecting onset of PTSD symptoms which could be the basis for developing remote and continuous monitoring systems for PTSD. Such systems may address a vital gap in just-in-time interventions for PTSD self-management outside of scheduled clinical appointments.

Understanding Heart Rate Reactions to Post-Traumatic Stress Disorder (PTSD) Among Veterans: A Naturalistic Study.

OBJECTIVE: We collected naturalistic heart rate data from veterans diagnosed with post-traumatic stress disorder (PTSD) to investigate the effects of various factors on heart rate. BACKGROUND: PTSD is prevalent among combat veterans in the United States. While a positive correlation between PTSD and heart rate has been documented, specific heart rate profiles during the onset of PTSD symptoms remain unknown. METHOD: Veterans were recruited during five cycling events in 2017 and 2018 to record resting and activity-related heart rate data using a wrist-worn device. The device also logged self-reported PTSD hyperarousal events. Regression analyses were performed on demographic and behavioral covariates including gender, exercise, antidepressants, smoking habits, sleep habits, average heart rate during reported hyperarousal events, age, glucocorticoids consumption, and alcohol consumption. Heart rate patterns during self-reported PTSD hyperarousal events were analyzed using Auto Regressive Integrated Moving Average (ARIMA). Heart rate data were also compared to an open-access non-PTSD representative case. RESULTS: Of 99 veterans with PTSD, 91 participants reported at least one hyperarousal event, with a total of 1023 events; demographic information was complete for 38 participants who formed the subset for regression analyses. The results show that factors including smoking, sleeping, gender, and medication significantly affect resting heart rate. Moreover, unique heart rate patterns associated with PTSD symptoms in terms of stationarity, autocorrelation, and fluctuation characteristics were identified. CONCLUSION: Our findings show distinguishable heart rate patterns and characteristics during PTSD hyperarousal events. APPLICATION: These findings show promise for future work to detect the onset of PTSD symptoms.

Bicyclist-evoked arousal and greater attention to bicyclists independently promote safer driving.

While attention has consistently been shown to be biased toward threatening objects in experimental settings, our understanding of how attention is modulated when the observer is in an anxious or aroused state and how this ultimately affects behavior is limited. In real-world environments, automobile drivers can sometimes carry negative perceptions toward bicyclists that share the road. It is unclear whether bicyclist encounters on a roadway lead to physiological changes and attentional biases that ultimately influence driving behavior. Here, we examined whether participants in a high-fidelity driving simulator exhibited an arousal response in the presence of a bicyclist and how this modulated eye movements and driving behavior. We hypothesized that bicyclists would evoke a robust arousal and orienting response, the strength of which would be associated with safer driving behavior. The results revealed that encountering a bicyclist evoked negative arousal by both self-report and physiological measures. Physiological and eye-tracking measures were themselves unrelated, however, being independently associated with safer driving behavior. Our findings offer a real-world demonstration of how arousal and attentional prioritization can lead to adaptive behavior.

Mitigating Drowsy Driving Risk for Night Shift Nurses.

Preventive measures range from napping to installing driver monitoring systems in cars.

The Impact of Commutes, Work Schedules, and Sleep on Near-Crashes during Nurses' Post Shift-Work Commutes: A Naturalistic Driving Study.

OCCUPATIONAL APPLICATIONSDriving and survey data were collected from nurses following the night-shift and analyzed with logistic regression and frequency analysis. The analyses showed that prior near-crashes and drive length contributed significantly to near-crashes. The frequency analysis showed that most near-crashes occurred on major roadways, including principal arterials, major collectors, and interstates, within the first 15 minutes of the drive. These results highlight the urgent need for countermeasures to prevent drowsy driving incidents among night-shift nurses. Specifically, nurses and hospital systems should focus on countermeasures that encourage taking a break on the post work commute and those that can intervene during the drive. This may include the use of educational programs to teach nurses the importance of adequate rest or taking a break to sleep during their drive home, or technology that can recognize drowsiness and alert nurses of their drowsiness levels, prompting them to take a break.

TECHNICAL ABSTRACTBackground Night-shift nurses are susceptible to drowsy driving crashes due to their long working hours, disrupted circadian rhythm, and reduced sleep hours. However, the extent to which work, sleep, and on-road factors impact the nurses' commutes and the occurrence of near-crash events is not well documented.Purpose A longitudinal naturalistic driving study with night-shift nurses from a large hospital in the United States was conducted to measure these factors and analyze the occurrence and location of near-crashes during post-shift commutes.Methods An on-board data recorder was used to record acceleration, speed, and GPS coordinates continuously. Nurses also completed daily surveys on their sleep, work, and commute. Near-crashes were identified from the data based on acceleration thresholds. Data from a total of 853 drives from 22 nurses and corresponding surveys were analyzed using Poisson and negative binomial regressions for swerve and hard brake near-crash events, respectively.Results Swerve events were increased by the length of the drive (RR = 2.59, LL = 1.62, UL = 4.16), and the occurrence of hard brakes (RR = 1.69, LL = 1.45, UL = 1.99), while hard brake events were increased by the occurrence of swerves (RR = 1.55, LL = 1.28, UL = 1.88). The majority of near-crashes occurred on principal arterials (n = 293), minor arterials (n = 71), and interstates (n = 51).Conclusions The results demonstrate the high risk of near-crashes during post-shift commutes, which may present danger to nurses and other drivers, and highlight the need for countermeasures that address shift structures, sleep quality, and taking breaks.

Steering or braking avoidance response in SHRP2 rear-end crashes and near-crashes: A decision tree approach.

OBJECTIVE: The paper presents a systematic analysis of drivers' crash avoidance response during crashes and near-crashes and developed a machine learning-based predictive model that can determine driver maneuver using pre-incident driver behavior and driving context. METHODS: We analyzed 286 naturalistic rear-end crashes and near-crashes from the SHRP2 naturalistic driving study. All the events were manually reduced using face video (face and forward) and kinematic responses. In this paper, we developed new reduction variables that enhanced the understanding of drivers' gaze behavior and roadway attention behavior during these events. These features reflected how the event criticality, measured using time to collision, related to drivers' pre-incident behavior (secondary behavior, gaze behavior), and drivers' perception of the event (physical reaction and maneuver). The imperative understanding of such relations was validated using a random forest- (RF) based classifier, which efficiently predicted if a driver was going to brake or change the lane as an avoidance maneuver. RESULTS: The RF presented in this paper effectively explored the nonlinear patterns in the data and was highly accurate (∼96 %) in its prediction. A further analysis of the RF model showed that six features played a pivotal role in the decision logic. These included the drivers' last glance duration before the event, last glance eccentricity, duration of 'eyes on road' immediately before the event, the time instance and criticality when the driver perceives the threat as well as acknowledge the threat, and possibility of an escape path in the adjacent lane. Using partial dependency plots, we also showed how different thresholds of these feature variables determined the drivers' maneuver intention. CONCLUSIONS: In this paper we analyzed driving context, drivers' behavior, event criticality, and drivers' response in a unified structure to predict their avoidance response. To the best of our knowledge, this is the first such effort where large-scale naturalistic data (crashes and near crashes) was analyzed for prediction of drivers' maneuver and determined key behavioral and contextual factors that contribute to this avoidance maneuver.